@@ -18,7 +18,7 @@ New updates and features include:
Please see our updated documentation pages in `docs/` for more details.
Development will be continuing on the `main` branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub, or in the [EleutherAI discord](discord.gg/eleutherai)!
Development will be continuing on the `main` branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub, or in the [EleutherAI discord](https://discord.gg/eleutherai)!
## Overview
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@@ -49,23 +49,28 @@ pip install -e .
We also provide a number of optional dependencies for extended functionality. Extras can be installed via `pip install -e ".[NAME]"`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models)(e.g. GPT-J-6B) on `hellaswag` you can use the following command:
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models)(e.g. GPT-J-6B) on `hellaswag` you can use the following command (this assumes you are using a CUDA-compatible GPU):
```bash
lm_eval --model hf \
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@@ -151,23 +156,28 @@ To call a hosted model, use:
```bash
export OPENAI_API_KEY=YOUR_KEY_HERE
lm_eval --model openai-completions \
--model_argsengine=davinci \
--model_argsmodel=davinci \
--tasks lambada_openai,hellaswag
```
Note that for externally hosted models, configs such as `--device` and `--batch_size` should not be used and do not function. Just like you can use `--model_args` to pass arbitrary arguments to the model constructor for local models, you can use it to pass arbitrary arguments to the model API for hosted models. See the documentation of the hosting service for information on what arguments they support.
We also support using your own local inference server with an implemented version of the OpenAI ChatCompletions endpoint and passing trained HuggingFace artifacts and tokenizers.
Note that for externally hosted models, configs such as `--device` and `--batch_size` should not be used and do not function. Just like you can use `--model_args` to pass arbitrary arguments to the model constructor for local models, you can use it to pass arbitrary arguments to the model API for hosted models. See the documentation of the hosting service for information on what arguments they support.
| API or Inference Server | Implemented? | `--model <xxx>` name | Models supported: | Request Types: |
| Your inference server here! | ... | ... | ... | ... | | ... |
| Mamba | :heavy_check_mark: | `mamba_ssm` | [Mamba architecture Language Models via the `mamba_ssm` package](https://huggingface.co/state-spaces) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
| Your local inference server! | :heavy_check_mark: | `local-chat-completions` (using `openai-chat-completions` model type) | Any server address that accepts GET requests using HF models and mirror's OpenAI's ChatCompletions interface | `generate_until` | | ... |
It is on our roadmap to create task variants designed to enable models which do not serve logprobs/loglikelihoods to be compared with generation performance of open-source models.
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@@ -225,9 +235,50 @@ Additionally, one can provide a directory with `--use_cache` to cache the result
For a full list of supported arguments, check out the [interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md) guide in our documentation!
## Visualizing Results
You can use [Zeno](https://zenoml.com) to visualize the results of your eval harness runs.
First, head to [hub.zenoml.com](https://hub.zenoml.com) to create an account and get an API key [on your account page](https://hub.zenoml.com/account).
Add this key as an environment variable:
```bash
export ZENO_API_KEY=[your api key]
```
You'll also need to install the `lm_eval[zeno]` package extra.
To visualize the results, run the eval harness with the `log_samples` and `output_path` flags.
We expect `output_path` to contain multiple folders that represent individual model names.
You can thus run your evaluation on any number of tasks and models and upload all of the results as projects on Zeno.
```bash
lm_eval \
--model hf \
--model_argspretrained=EleutherAI/gpt-j-6B \
--tasks hellaswag \
--device cuda:0 \
--batch_size 8 \
--log_samples\
--output_path output/gpt-j-6B
```
Then, you can upload the resulting data using the `zeno_visualize` script:
```bash
python scripts/zeno_visualize.py \
--data_path output \
--project_name"Eleuther Project"
```
This will use all subfolders in `data_path` as different models and upload all tasks within these model folders to Zeno.
If you run the eval harness on multiple tasks, the `project_name` will be used as a prefix and one project will be created per task.
You can find an example of this workflow in [examples/visualize-zeno.ipynb](examples/visualize-zeno.ipynb).
## How to Contribute or Learn More?
For more information on the library and how everything fits together, check out all of our [documentation pages](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor/docs)! We plan to post a larger roadmap of desired + planned library improvements soon, with more information on how contributors can help.
For more information on the library and how everything fits together, check out all of our [documentation pages](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs)! We plan to post a larger roadmap of desired + planned library improvements soon, with more information on how contributors can help.
@@ -4,7 +4,7 @@ Welcome to the docs for the LM Evaluation Harness!
## Table of Contents
* To learn about the public interface of the library, as well as how to evaluate via the commandline or as integrated into an external library, see the [Interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/user_guide.md)
* To learn about the public interface of the library, as well as how to evaluate via the commandline or as integrated into an external library, see the [Interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/interface.md)
* To learn how to add a new library, API, or model type to the library, as well as a quick explainer on the types of ways to evaluate an LM, see the [Model Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/model_guide.md).
* For a crash course on adding new tasks to the library, see our [New Task Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/new_task_guide.md).
* To learn more about pushing the limits of task configuration that the Eval Harness supports, see the [Task Configuration Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/task_guide.md).
Open the file specified at the `--output_base_path <path>` and ensure it passes
a simple eye test.
## Versioning
One key feature in LM Evaluation Harness is the ability to version tasks--that is, mark them with a specific version number that can be bumped whenever a breaking change is made.
This version info can be provided by adding the following to your new task config file:
```
metadata:
version: 0
```
Now, whenever a change needs to be made to your task in the future, please increase the version number by 1 so that users can differentiate the different task iterations and versions.
If you are incrementing a task's version, please also consider adding a changelog to the task's README.md noting the date, PR number, what version you have updated to, and a one-liner describing the change.
for example,
* \[Dec 25, 2023\] (PR #999) Version 0.0 -> 1.0: Fixed a bug with answer extraction that led to underestimated performance.
## Checking performance + equivalence
It's now time to check models' performance on your task! In the evaluation harness, we intend to support a wide range of evaluation tasks and setups, but prioritize the inclusion of already-proven benchmarks following the precise evaluation setups in the literature where possible.
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@@ -340,4 +359,4 @@ It is recommended to include a filled-out copy of this checklist in the README.m
## Submitting your task
You're all set! Now push your work and make a pull request to the `big-refactor` branch! Thanks for the contribution :). If there are any questions, please leave a message in the `#lm-thunderdome` channel on the EAI discord!
You're all set! Now push your work and make a pull request to the `main` branch! Thanks for the contribution :). If there are any questions, please leave a message in the `#lm-thunderdome` channel on the EAI discord!
"Benchmarking your models is the first step towards making sure your model performs well.\n",
"However, looking at the data behind the benchmark, slicing the data into subsets, and comparing models on individual instances can help you even more in evaluating and quantifying the behavior of your AI system.\n",
"\n",
"All of this can be done in [Zeno](https://zenoml.com)!\n",
"Zeno is super easy to use with the eval harness, let's explore how you can easily upload and visualize your eval results.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install this project if you did not already do that. This is all that needs to be installed for you to be able to visualize your data in Zeno!\n",
"!pip install -e ..\n",
"!pip install -e ..[zeno]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run the Eval Harness\n",
"\n",
"To visualize the results, run the eval harness with the `log_samples` and `output_path` flags. We expect `output_path` to contain multiple folders that represent individual model names. You can thus run your evaluation on any number of tasks and models and upload all of the results as projects on Zeno.\n"
"This is so you can be authenticated with Zeno.\n",
"If you don't already have a Zeno account, first create an account on [Zeno Hub](https://hub.zenoml.com).\n",
"After logging in to Zeno Hub, generate your API key by clicking on your profile at the bottom left to navigate to your account page.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%env ZENO_API_KEY=YOUR_API_KEY"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize Eval Results\n",
"\n",
"You can now use the `zeno_visualize` script to upload the results to Zeno.\n",
"\n",
"This will use all subfolders in `data_path` as different models and upload all tasks within these model folders to Zeno. If you run the eval harness on multiple tasks, the `project_name` will be used as a prefix and one project will be created per task.\n"
